相关论文: Spatial Signal Design for Positioning via End-to-E…
Motivated by the ever-growing demand for \emph{green} wireless communications and the advantages of \emph{cell-free} (CF) massive multiple-input multiple-output (MIMO) systems, we focus on the design of their downlink for optimal…
For numerous earth observation applications, one may benefit from various satellite sensors to address the reconstruction of some process or information of interest. A variety of satellite sensors deliver observation data with different…
End to end learning is machine learning starting in raw data and predicting a desired concept, with all steps done automatically. In software engineering context, we see it as starting from the source code and predicting process metrics.…
Precise parking requires an end-to-end system where perception adaptively provides policy-relevant details - especially in critical areas where fine control decisions are essential. End-to-end learning offers a unified framework by directly…
This paper considers the beamforming design for a multiuser multiple-input single-output (MISO) downlink with an arbitrary number of (context-specific) shaping constraints. In this setup, the state-of-the-art beamforming schemes cannot…
Mobile edge computing (MEC) is a key player in low latency 5G networks with the task to resolve the conflict between computationally-intensive mobile applications and resource-limited mobile devices (MDs). As such, there has been intense…
This work advocates the use of deep learning to perform max-min and max-prod power allocation in the downlink of Massive MIMO networks. More precisely, a deep neural network is trained to learn the map between the positions of user…
High-precision positioning is vital for cellular networks to support innovative applications such as extended reality, unmanned aerial vehicles (UAVs), and industrial Internet of Things (IoT) systems. Existing positioning algorithms using…
Massive MIMO is a promising technique to meet the exponential growth of global mobile data traffic demand. However, contrary to the current systems, energy consumption of next generation networks is required to be load adaptive as the…
This paper investigates multimodal semantic non-orthogonal transmission and fusion in hybrid analog-digital massive multiple-input multiple-output (MIMO). A Transformer-based cross-modal source-channel semantic-aware network (CSC-SA-Net)…
End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical…
In the field of autonomous driving, end-to-end deep learning models show great potential by learning driving decisions directly from sensor data. However, training these models requires large amounts of labeled data, which is time-consuming…
Integrated sensing and communication holds great promise for low-altitude economy applications. However, conventional downtilted base stations primarily provide sectorized forward lobes for ground services, failing to sense air targets due…
In edge intelligence, deep learning~(DL) models are deployed at an edge device and an edge server for data processing with low latency in the Internet of Things~(IoT). In this letter, we propose a new end-to-end learning-based wireless…
Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks often solely rely on location-specific predictors that…
The concept of semantic communication provides a novel approach for applications in scenarios with limited communication resources. In this paper, we propose an end-to-end (E2E) semantic molecular communication system, aiming to enhance the…
This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enable handoffs between beams that belong to the same or…
Energy efficiency (EE) and spectral efficiency (SE) are two of the key performance metrics in future wireless networks, covering both design and operational requirements. For previous conventional resource allocation techniques, these two…
In network MIMO cellular systems, subsets of base stations (BSs), or remote radio heads, are connected via backhaul links to central units (CUs) that perform joint encoding in the downlink and joint decoding in the uplink. Focusing on the…
The end-to-end learning of Simultaneous Wireless Information and Power Transfer (SWIPT) over a noisy channel is studied. Adopting a nonlinear model for the energy harvester (EH) at the receiver, a joint optimization of the transmitter and…